The evolution of aerospace systems has reached a critical inflection point where autonomous perception capabilities now fundamentally determine operational success across both orbital and terrestrial domains. Unsupervised Computer Vision for Aerospace Systems emerges simultaneously as both a response to and catalyst for this paradigm shift—presenting methodological frameworks that transcend conventional supervised learning constraints through self-organizing visual intelligence. This treatise articulates a fundamental reimagining of spacecraft perception, where adaptive algorithms transform raw sensor data into mission-critical insights without dependence on manually curated annotations or idealized operational conditions.